# TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation

arXiv: https://arxiv.org/abs/2006.06434 · HTML: https://mlsanity.com/paper/2006.06434/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 12 of 12)

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- [Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question](https://mlsanity.com/paper/2201.01209/) (2022)
- [MMESGBench: Pioneering Multimodal Understanding and Complex Reasoning Benchmark for ESG Tasks](https://mlsanity.com/paper/2507.18932/) (2025)
- [AnnotatedTables: A Large Tabular Dataset with Language Model Annotations](https://mlsanity.com/paper/2406.16349/) (2024)
- [AskYourDB: An end-to-end system for querying and visualizing relational databases using natural language](https://mlsanity.com/paper/2210.08532/) (2022)
- [V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization](https://mlsanity.com/paper/2604.20755/) (2026)

## Abstract (arXiv)

Parsing natural language to corresponding SQL (NL2SQL) with data driven approaches like deep neural networks attracts much attention in recent years. Existing NL2SQL datasets assume that condition values should appear exactly in natural language questions and the queries are answerable given the table. However, these assumptions may fail in practical scenarios, because user may use different expressions for the same content in the table, and query information outside the table without the full picture of contents in table. Therefore we present TableQA, a large-scale cross-domain Natural Language to SQL dataset in Chinese language consisting 64,891 questions and 20,311 unique SQL queries on over 6,000 tables. Different from exisiting NL2SQL datasets, TableQA requires to generalize well not only to SQL skeletons of different questions and table schemas, but also to the various expressions for condition values. Experiment results show that the state-of-the-art model with 95.1% condition value accuracy on WikiSQL only gets 46.8% condition value accuracy and 43.0% logic form accuracy on TableQA, indicating the proposed dataset is challenging and necessary to handle. Two table-aware approaches are proposed to alleviate the problem, the end-to-end approaches obtains 51.3% and 47.4% accuracy on the condition value and logic form tasks, with improvement of 4.7% and 3.4% respectively.